class_docstr: Gaussian policy that consists of fully-connected layers.
  
  This model has two output layers: the mean layer and the variance layer.
  The mean of the Gaussian is computed as follows:
      Let y as the output of the mean layer.
      If bound_mean=False:
          mean = y (if bound_mean=False)
  
      If bound_mean=True:
          mean = min_action + tanh(y) * (max_action - min_action) / 2
  
  The variance of the Gaussian is computed as follows:
      Let y as the output of the variance layer.
      variance = softplus(y) + min_var
  
  Args:
      n_input_channels (int): Number of input channels.
      action_size (int): Number of dimensions of the action space.
      n_hidden_layers (int): Number of hidden layers.
      n_hidden_channels (int): Number of hidden channels.
      min_action (ndarray): Minimum action. Used only when bound_mean=True.
      max_action (ndarray): Maximum action. Used only when bound_mean=True.
      var_type (str): Type of parameterization of variance. It must be
          'spherical' or 'diagonal'.
      nonlinearity (callable): Nonlinearity placed between layers.
      mean_wscale (float): Scale of weight initialization of the mean layer.
      var_wscale (float): Scale of weight initialization of the variance
          layer.
      var_bias (float): The initial value of the bias parameter for the
          variance layer.
      min_var (float): Minimum value of the variance.
code: def compute_mean_and_var(self, x):
      h = x
      for layer in self.hidden_layers:
          h = self.nonlinearity(layer(h))
      mean = self.mean_layer(h)
      if self.bound_mean:
          mean = bound_by_tanh(mean, self.min_action, self.max_action)
      var = (
          F.broadcast_to(F.softplus(self.var_layer(h)), mean.shape)
          + self.min_var
      )
      return mean, var
  
imports: from logging import getLogger
  import chainer
  from chainer import functions
  from chainer.initializers import LeCunNormal
  from chainer import links
  import numpy
  from chainerrl import distribution
  from chainerrl.functions.bound_by_tanh import bound_by_tanh
  from chainerrl import links
  from chainerrl.policy import Policy
label_desc: Use image node_id 2 for calling the FCGaussianPolicy obj's underlying member method code with example usage: obj.compute_mean_and_var(x) and returns: mean,  var